File size: 3,045 Bytes
127449b 2536cdc 127449b 2536cdc 127449b 2536cdc 437c7b7 c432972 437c7b7 2536cdc 437c7b7 875c0b6 437c7b7 875c0b6 437c7b7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
---
base_model:
- Qwen/Qwen2.5-Coder-3B-Instruct
datasets:
- TIGER-Lab/VisCode-200K
language:
- en
license: apache-2.0
tags:
- code
library_name: transformers
pipeline_tag: text-generation
---
# VisCoder-3B
[🏠 Project Page](https://tiger-ai-lab.github.io/VisCoder) | [📖 Paper](https://arxiv.org/abs/2506.03930) | [💻 GitHub](https://github.com/TIGER-AI-Lab/VisCoder) | [🤗 VisCode-200K](https://huggingface.co/datasets/TIGER-Lab/VisCode-200K) | [🤗 VisCoder-7B](https://huggingface.co/TIGER-Lab/VisCoder-7B)
**VisCoder-3B** is a lightweight language model fine-tuned for **Python visualization code generation and iterative correction**. It is trained on **VisCode-200K**, a large-scale instruction-tuning dataset that integrates natural language instructions, validated Python code, and execution-guided revision supervision.
## 🧠 Model Description
**VisCoder-3B** is trained on **VisCode-200K**, a large-scale instruction-tuning dataset tailored for executable Python visualization tasks. It addresses a core challenge in data analysis: generating Python code that not only executes successfully but also produces **semantically meaningful plots** by aligning **natural language instructions**, **data structures**, and **visual outputs**.
We propose a **self-debug evaluation protocol** that simulates real-world developer workflows. In this setting, models are allowed to revise previously failed generations over multiple rounds with guidance from **execution feedback**.
## 📊 Main Results on PandasPlotBench
We evaluate VisCoder-3B on [**PandasPlotBench**](https://github.com/TIGER-AI-Lab/VisCoder/tree/main/eval), which tests executable visualization code generation across **Matplotlib**, **Seaborn**, and **Plotly**. Evaluation includes both standard generation and **multi-turn self-debugging**
> VisCoder-3B outperforms existing open-source baselines on multiple libraries and shows consistent recovery improvements under the self-debug protocol.
## 📁 Training Details
- **Base model**: Qwen2.5-Coder-3B-Instruct
- **Framework**: [ms-swift](https://github.com/modelscope/swift)
- **Tuning method**: Full-parameter supervised fine-tuning (SFT)
- **Dataset**: [VisCode-200K](https://huggingface.co/datasets/TIGER-Lab/VisCode-200K), which includes:
- 150K+ validated Python visualization samples with corresponding images
- 45K+ multi-turn correction dialogues guided by execution results
## 📖 Citation
If you use VisCoder-3B or VisCode-200K in your research, please cite:
```bibtex
@article{ni2025viscoder,
title={VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation},
author={Ni, Yuansheng and Nie, Ping and Zou, Kai and Yue, Xiang and Chen, Wenhu},
journal={arXiv preprint arXiv:2506.03930},
year={2025}
}
```
For evaluation scripts and more information, see our [GitHub repository](https://github.com/TIGER-AI-Lab/VisCoder). |